CycleGAN-Based Global Image Translation. We keep the model structure for local image reconstruction in our model as a generator, and add an additional discriminator and the losses of CycleGAN [1]} to generate new tumors in a normal image. We also applied spatial attention [2]} into the dilated convolution layers of the generator in CycleGAN to see the performance. The results show that the output images are blur even with many rounds \((>10^4)\)  of training and there are no visible tumors in the output images, partially because of the diversity of tumor sizes and relative positions in cancer images. Moreover, spatial attention for high-resolution images is highly computationally complicated, which leads to slow running speeds and higher demands of GPU resources.
